ArticleNature communications2022
Language models can learn complex molecular distributions.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
71 citing papers in PubMed.
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- Mol-CADiff: text-conditional molecule generation via causality-aware autoregressive diffusion.Nature communications · 2026Article
- Efficient and valid large molecule generation via self-supervised generative models.npj drug discovery · 2026Article
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors.bioRxiv : the preprint server for biology · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- MolOrgGPT:Journal of chemical information and modeling · 2026Article
- Knowledgeable Language Models as Black-Box Optimizers for Personalized Medicine.... International Conference on Learning Representations · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- All-atom protein sequence design using discrete diffusion models.Journal of cheminformatics · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ACS central science · 2025Article
- Systematic Exploration of Small-Molecule Binding via a Large Language Model Trained on Textualized Protein-Ligand Interactions.Molecules (Basel, Switzerland) · 2025Article
- Beyond performance: how design choices shape chemical language models.Journal of cheminformatics · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ArXiv · 2025Article
- How evaluation choices distort the outcome of generative drug discovery.Journal of cheminformatics · 2025Article
- Stereochemistry-aware string-based molecular generation.PNAS nexus · 2025Article
- VeGA: A Versatile Generative Architecture for Bioactive Molecules across Multiple Therapeutic Targets.Journal of chemical information and modeling · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compounds, depends on their ability to learn a training distribution of molecules. The most simple example is a language model that takes the form of a recurrent neural network and generates molecules using a string representation. Since their initial use, subsequent work has shown that language models are very capable, in particular, recent research has demonstrated their utility in the low data regime. In this work, we investigate the capacity of simple language models to learn more complex distributions of molecules. For this purpose, we introduce several challenging generative modeling tasks by compiling larger, more complex distributions of molecules and we evaluate the ability of language models on each task. The results demonstrate that language models are powerful generative models, capable of adeptly learning complex molecular distributions. Language models can accurately generate: distributions of the highest scoring penalized LogP molecules in ZINC15, multi-modal molecular distributions as well as the largest molecules in PubChem. The results highlight the limitations of some of the most popular and recent graph generative models- many of which cannot scale to these molecular distributions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.